To survive today companies must continuously improve quality and reduce the variability in every process throughout the organization. Access to data and the capability to analyze that data are the keys to reducing defects and increasing customer satisfaction, thus leading to increased productivity and greater top and bottom line growth.
Conventional business intelligence systems (BIS) allow a company to view and analyze performance measures across a business enterprise. These systems have useful functions such as: an ability to view data in a graphical or tabular format, to drill down to lower levels of data, and to alert the user if a performance measurement goes outside a target value. Some conventional systems are web-based and provide a configurable home page. Some conventional systems may have a library of performance measures or key performance indicators to benchmark actual performance against multiple targets. However, these conventional systems do not have the capability to present complex statistical analysis in a user friendly format, which is crucial to reducing defects and improving processes.
Referring to FIG. 1, a typical BIS report offers a view of a given performance measure, for example, book-to-ship cycle time. It provides a standard bar or line graph 170 to view the actual data 150 and user defined targets 152. The variance, however, is only listed in the table 160 below the graph 170, which also includes sums or averages of each column. To allow the user flexibility, there are many dimensions 154 (e.g., time, geography, etc.) by which to filter and view the report.
Viewing performance measures with line graphs, bar charts, pie charts, etc. provides a standardized way of analyzing data, but in many scenarios is not enough to get a complete and thorough understanding of the actual process capability. Often, more advanced statistics are required to understand the entire issue or discover opportunities that would normally be overlooked. For example, because the average is within the target range, the data may appear acceptable. However, many data points may actually be outside of the target range. Thus, problem data may be overlooked.
There also exist conventional ad-hoc statistical software packages; however, these packages have limitations as well. Such statistical software packages may be able to assist in measuring the quality of a given process or product. For example, they may calculate a sigma value, which is a statistical parameter that corresponds to a standard deviation on a bell curve. The number of deviations between the statistical mean and the customer defined limits of acceptability provides a quantifiable measurement of process performance. As the process capability increases, so does the sigma level of the process. A process that performs at the six sigma level has six standard deviations between the mean and the customer defined limits, and corresponds to 3.4 defects per million, assuming a normal distribution.
However, to perform a six sigma analysis ideally requires involvement of the entire organization, and hence requires access to information relative to a practical measure (e.g., book-to-ship cycle time), which may be distributed across an organization. Unfortunately, these conventional ad-hoc statistical systems do not generally have access to enterprise wide data, without having to download the data to the ad-hoc statistical software package. Downloading such data is often complex, as it may be stored across many databases across the world, and may be in different formats. Thus, the data may also need to be re-formatted before an analysis may be done. Additionally, some conventional statistical packages require the user to have a considerable knowledge of statistics to be able to produce a usable report. Furthermore, the statistical programs themselves may need to be loaded on each computer upon which an analysis is to be run, which is time consuming and costly.